U.S. patent number 8,032,401 [Application Number 11/779,632] was granted by the patent office on 2011-10-04 for system and method to calculate procurement of assets.
This patent grant is currently assigned to General Electric Company. Invention is credited to Suresh K. Choubey.
United States Patent |
8,032,401 |
Choubey |
October 4, 2011 |
System and method to calculate procurement of assets
Abstract
A system and method to calculate a mode of procurement of at
least one asset is provided. The system comprises a tracking
element operable to generate a signal representative of a location
of the at least one asset, and a controller in communication with
the at least one tracking element. The controller includes a
processor operable to execute program instructions representative
of the acts of measuring a utilization of the at least one asset
having a unique identifier over at least one time interval,
calculating a projected need of the at least one asset over a
predetermined future time interval dependent on the utilization of
the at least one asset, calculating a mode of procurement of the
projected need of the at least one asset dependent on the projected
need of the at least one asset, and displaying the mode of
procurement to the user.
Inventors: |
Choubey; Suresh K. (Delafield,
WI) |
Assignee: |
General Electric Company
(Schenectady, NY)
|
Family
ID: |
40265602 |
Appl.
No.: |
11/779,632 |
Filed: |
July 18, 2007 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20090024491 A1 |
Jan 22, 2009 |
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Current U.S.
Class: |
705/7.11;
340/572.1; 706/15; 705/307 |
Current CPC
Class: |
G06Q
10/06 (20130101); G06Q 30/0645 (20130101); G06Q
10/087 (20130101); G06Q 10/063 (20130101) |
Current International
Class: |
G06F
17/50 (20060101); G06F 9/44 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Rent VS Buy Calculator by RSC Equipment Rental dated Jun. 15, 2006
(http://web.archive.org/web/20061015163820/http://www.rscrental.content/R-
entEquipment/whyRentvsbuy.aspx. cited by examiner .
Buy, Lease, or Rent by Masonry Magazine dated 2005
(http:masonrymagazine.com/11-05/buyleaserentn.html). cited by
examiner .
The Equipment Equation by Grounds Maintenance dated Nov. 1, 2005
(http://web.archive.org/web/2006011008312/http://grounds-mag.com/mag/grou-
nds.sub.--maintenance.sub.--equipment.sub.--equation/). cited by
examiner .
Approximations Algorithms, by Don Sheehy, dated Oct. 17, 2005,
(http://www.cs.cmu.edu/afs/cs/academic/class/15854-f05/www/scribe/lec11.p-
s). cited by examiner .
Test Equipment: Weighing The Rent or Buy Decision by Anthony M.
Schiavo, dated Dec. 1, 1975. cited by examiner.
|
Primary Examiner: Champagne; Luna
Claims
What is claimed is:
1. A system to calculate a mode of procurement of at least one
asset, the system comprising: at least one tracking element
operable to generate a signal representative of a location of the
at least one asset; and a controller in communication with the at
least one tracking element, the controller including a processor in
communication with a memory, the processor operable to execute a
plurality of program instructions stored in the memory, the
plurality of program instructions representative of the acts of:
measuring a utilization of the at least one asset having a unique
identifier over at least one time interval; calculating a projected
need of the at least one asset over a predetermined future time
interval based upon the measured utilization of the at least one
asset over the at least one time interval; calculating a mode of
procurement of the at least one asset based upon the projected need
of the at least one asset using a recurrent neural network
algorithm which comprises calculating automatically adjustable
empirical weights using back-propagation, wherein back-propagation
comprises using a predetermined error of historical input values to
produce a historical outcome; adopting the automatically adjustable
empirical weights into the recurrent neural algorithm; and
displaying the mode of procurement to the user; wherein the act of
calculating the projected need is based upon at least one parameter
from the group comprising an upgrade factor of switching from an
existing asset to a new asset, a predicted useful life of the at
least one asset, a business adjustment factor representative of a
business direction indicative of expansion of patient services in
view of local competition, and a patient increase factor (PIF)
representative of a projected increase in patient care.
2. The system of claim 1, wherein the act of measuring the
utilization of the at least one asset includes calculating at least
one average utilization of the at least one asset over a
predetermined time period on a continuous basis, and wherein the
mode of procurement includes one of the group consisting of a
purchase of the at least one asset and a rental of the at least one
asset.
3. The system of claim 1, wherein the act of calculating the mode
of procurement of the at least one asset further comprises using a
plurality of parameter values of projected utilization of the at
least one asset over different time intervals.
4. The system of claim 1, wherein an outcome of the recurrent
neural network algorithm is a binary value representative of the
mode of procurement of the at least one asset.
5. The system of claim 1, the plurality of program instructions
further representative of the acts of: testing whether the
recurrent neural network algorithm produces an outcome of the mode
of procurement within a predetermined error of other historically
known outcomes of the mode of procurement correlated to other
historically known needs of the at least one asset.
6. The system of claim 1, the plurality of program instructions
further representative of the acts of: comparing the projected need
of the at least one asset of a first user to the measured
utilization of at least one other asset by a second user, the
second user different than the first user and the at least one
other asset of a same type as the at least one asset of the first
user.
7. The system of claim 1, wherein the act of calculating the
projected need of the at least one asset over the predetermined
future time interval is based upon a trend of utilization of the at
least one asset calculated over a predetermined historical time
interval.
8. The system of claim 1, wherein the act of calculating the mode
of procurement of the at least one asset is based upon at least one
parameter of the group comprising a rental cost, a purchase cost,
and a depreciation rate of the at least one asset over the future
time interval.
9. The system of claim 1, wherein the act of calculating a mode of
procurement includes executing a procurement algorithm that
includes at least one parameter of the group consisting of a
measured utilization of the at least one asset and a predicted
business direction of services correlated with utilization of the
at least one asset.
10. A computer implemented method of calculating a mode of
procurement of at least one asset having at least one tracking
element, the method comprising the acts of: communicating with the
at least one tracking element for the at least one asset to
identify a location of the at least one asset via at least one
programmed computer; measuring a utilization of the at least one
asset having a unique identifier over at least one time interval;
calculating a projected need of the at least one asset over a
predetermined future time interval based upon the measured
utilization of the at least one asset; calculating a mode of
procurement of the at least one asset based upon the projected need
of the at least one asset using a recurrent neural network
algorithm which comprises calculating automatically adjustable
empirical weights using back-propagation, wherein back-propagation
comprises using a predetermined error of historical input values to
produce a historical outcome; adopting the automatically adjustable
weights into the recurrent neural algorithm; and displaying on a
computer display the mode of procurement to the user; wherein the
act of calculating the projected need is based upon at least one
parameter from the group comprising an upgrade factor of switching
from an existing asset to a new asset, a predicted useful life of
the at least one asset, a business adjustment factor representative
of a business direction indicative of expansion of patient services
in view of local competition, and a patient increase factor (PIF)
representative of a projected increase in patient care.
11. The method of claim 10, wherein the act of measuring the
utilization of the at least one asset includes calculating at least
one average utilization of the at least one asset over a
predetermined time period on a continuous basis, and wherein the
mode of procurement includes one of the group comprising a purchase
of the at least one asset and a rental of the at least one
asset.
12. The method of claim 10, wherein the act of calculating the mode
of procurement of the at least one asset further comprises using a
plurality of parameters of projected utilization of the least one
asset over different time intervals.
13. The method of claim 10, wherein an outcome of the recurrent
neural network algorithm is a binary value representative of a mode
of procurement of the at least one asset.
14. The method of claim 10, the method further including the act of
testing whether the recurrent neural network algorithm produces an
outcome of the mode of procurement within a predetermined error of
other historically known outcomes of the mode of procurement
correlated to other historically known needs of the at least one
asset.
15. The method of claim 10, the method further including the act of
comparing the projected need of the at least one asset of a first
user to the measured utilization of at least another asset by a
second user, the second user different than the first user and the
at least another asset of a same type as the at least one asset of
the first user.
16. The method of claim 10, wherein the act of calculating the
projected need of the at least one asset over the predetermined
future time interval is based upon a trend of utilization of the at
least one asset calculated over a predetermined historical time
interval.
Description
BACKGROUND OF THE INVENTION
This invention generally relates to a system for and method of
managing procurement of assets, and more particularly, to a system
and method to predict or forecast needs to purchase or rent assets,
and to optimize the capital and operational expenses of an
enterprise.
Larger industrial, healthcare or commercial facilities can be
spread out over a large campus and include multiple floors each
having multiple rooms. Each of the facilities can employ various
assets used in manufacturing or providing services. For example, a
healthcare facility or hospital employs numerous assets that can be
spread out over a large campus and/or moved from room to room.
Examples of assets include intravenous pumps, wheel chairs, digital
thermometers, local patient monitors, patient bed, ventilators,
etc. A similar scenario can be said for an industrial facility that
includes various portable pumps, hoists, winches, etc.
BRIEF DESCRIPTION OF THE INVENTION
Facilities typically acquire or purchase assets on a purely
speculative basis. There is a need for a system to improve
efficiency in the purchase of assets on a per department basis that
does not rely on pure speculation. There is also a need for a
system to manage purchasing of assets that will improve efficiency
in the purchase of assets on an overall basis for a series of
individual departments comprising the facility. There is also a
need to minimize the capital and operational expenditures while
maintaining the inventory and working condition of the equipment
for proper functioning and improved efficiency of the
enterprise.
The above-mentioned shortcomings, disadvantages and problems are
addressed by the embodiments described herein in the following
description.
An embodiment of a system to calculate a mode of procurement of at
least one asset is provided. The system comprises at least one
tracking element operable to generate a signal representative of a
location of the at least one asset; and a controller in
communication with the at least one tracking element. The
controller includes a processor in communication with a memory, the
processor operable to execute a plurality of program instructions
stored in the memory. The plurality of program instructions are
representative of the acts of measuring a utilization of the at
least one asset having a unique identifier over at least one time
interval, calculating a projected need of the at least one asset
over a predetermined future time interval dependent on the
utilization of the at least one asset, calculating a mode of
procurement of the projected need of the at least one asset
dependent on the projected need of the at least one asset, and
displaying the mode of procurement to the user.
An embodiment of a method of calculating a mode of procurement of
at least one asset is provided. The method comprises the acts of
measuring a utilization of the at least one asset having a unique
identifier over at least one time interval; calculating a projected
need of the at least one asset over a predetermined future time
interval dependent on the utilization of the at least one asset;
calculating a mode of procurement of the projected need of the at
least one asset dependent on the projected need of the at least one
asset, and displaying the mode of procurement to the user.
Systems and methods of varying scope are described herein. In
addition to the aspects and advantages described in this summary,
further aspects and advantages will become apparent by reference to
the drawings and with reference to the detailed description that
follows.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 shows a schematic block diagram of an embodiment of a system
operable to manage purchasing of assets of a healthcare
facility.
FIG. 2 illustrates an embodiment of a method to calculate a mode of
procurement of a projected need of the assets of the system
described in FIG. 1.
FIG. 3 shows an embodiment of a schematic block diagram
illustrative of tracking a utilization of the assets described in
FIG. 1.
FIG. 4 shows a schematic diagram illustrative of an embodiment of a
recurrent neural algorithm operable to calculate a mode to procure
a projected need of an asset.
FIG. 5 illustrates a schematic diagram illustrative of an
embodiment to validate the algorithm of FIG. 4 dependent on
historical outcomes of a mode of procurement correlated to a known
input.
DETAILED DESCRIPTION OF THE INVENTION
In the following detailed description, reference is made to the
accompanying drawings that form a part hereof, and in which is
shown by way of illustration specific embodiments, which may be
practiced. These embodiments are described in sufficient detail to
enable those skilled in the art to practice the embodiments, and it
is to be understood that other embodiments may be utilized and that
logical, mechanical, electrical and other changes may be made
without departing from the scope of the embodiments. The following
detailed description is, therefore, not to be taken in a limiting
sense.
FIG. 1 illustrates one embodiment of a system 100 to manage
purchasing of assets 105, 110 and 115 at a facility or entity.
Examples of the facility or entity can be a hospital, clinic or
other healthcare provider with one or more patient rooms or
departments located over one or more buildings. The entity can also
include a cluster of hospitals or clinics or combination thereof of
designated parameter (e.g., number of patient rooms, number of
departments, number of patients, etc.) of similar comparison.
An embodiment of the series of assets 105, 110 and 115 can include
medical devices or entities which have value, and which are
affiliated with the patient experience at a hospital, clinic, or
other type of healthcare facility. An embodiment of the first asset
105 can be an intravenous pump, the second asset 110 can be a
wheelchair, and the third asset 115 can be a healthcare personnel,
or can represent a type of a series or group thereof. The assets
105, 110, and 115 can be stationary or mobile. Yet, the number and
types of assets 105, 110 and 115 can vary. Although the following
description is in reference to assets 105, 110 and 115 associated
with a hospital or healthcare facility, it should be understood
that the subject matter is not so limited. The assets 105, 110 and
115 can be associated with various industrial or commercial
environments or facilities.
The system 100 includes a series of tracking elements 125, 130, and
135 located for each asset 105, 110 and 115, respectively. The
tracking elements 125, 130, and 135 are generally operable to
create a signal indicative of a location or state of the respective
assets 105, 110 and 115. Examples of the tracking elements 125,
130, and 135 can include a geographic positioning system (GPS)
receiver in communication with a satellite, electromagnetic
receivers and transmitters, radio frequency identification (RFID)
tags, radio frequency (rf) transmitters and receivers, bar code, or
the like or combination thereof operable to locate a position
(e.g., a room location at a facility, a geographic location having
a latitude and longitude, a coordinate, etc.) of the respective
assets 105, 110, and 115 relative to a reference. The type of
technique of tracking (e.g., electromagnetic, optical, global
positioning relative to a satellite, etc.) can vary.
The system 100 further includes a controller 150 in communication
with the tracking elements 125, 130, and 135 so as to track
movement of the assets 105, 110 and 115 between various states or
locations. The communication of the controller 150 with the
tracking elements 125, 130, and 135 can be via a wireless
connection (e.g., radio frequency, etc.) or wired connection (e.g.,
communication bus, etc.) or combination thereof to track movement
of the series of assets 105, 110 and 115. Communication can be
direct, or over an Internet network or an Ethernet network or a
local area network (LAN).
An embodiment of the controller 150 can include a computer in a
desktop configuration or laptop configuration. Yet, the type of
controller 150 can vary. The controller 150 generally includes one
or more processors 155 in communication with a memory 160 having a
computer-readable storage medium (e.g., compact disc (CD), DVD,
memory stick, random access memory (RAM), random operating memory
(ROM), etc.). The storage medium is generally operable to receive
and record a plurality of programmable instructions for execution
by the processor 155.
An embodiment of the controller 150 is also connected in
communication with an input device 165 and an output device 170.
The input device 165 can include one or combination of a keyboard,
touch-screen, remote computer workstation, mouse, joystick, tracker
ball, etc. or the like operable to receive data from an operator.
The output device 170 can include a display comprising one or
combination of a monitor, an alarm, light emitting diodes (LEDs),
printer, audible speaker, pager, personal data assistant (PDA),
etc. operable to visually or audibly show an output of the
controller 150 for illustration to an operator. The controller 150
can also be connected in communication with a remote computer or
workstation (not shown).
Having described a general construction of one embodiment of the
system 100, the following is a general description of an embodiment
of a method 200 to calculate a mode or manner or fashion to procure
one or more of the assets 105, 110, and 115 at the facility, as
illustrated in FIG. 2. It should also be understood that the
sequence of the acts or steps of the method 200 as discussed in the
foregoing description can vary. Also, it should be understood that
the method 200 may not require each act or step in the foregoing
description, or may include additional acts or steps not disclosed
herein.
FIG. 2 illustrates one embodiment of the method 200. Act 205
includes the start of the method 200. Act 210 includes tracking a
location of each asset 105, 110, and 115. An embodiment of act 210
includes receiving a signal via the tracking element 125, 130, and
135 representative of a location data of each asset 105, 110, and
115 in combination with a unique identifier of the asset 105, 110,
and 115 on a periodic or continuous basis. Act 210 can further
include receiving a status or health (e.g., in need of repair, in
need of maintenance, etc.) of the asset 105, 110, and 115.
Act 215 includes tracking or measuring utilization of each asset
105, 110, and 115 or type thereof and communicating the measurement
data to the controller 150. An embodiment of act 215 includes
calculating a utilization of assets of each type or category of
assets (e.g., intravenous pumps, wheelchairs, etc.) and on an
individual asset basis (e.g., wheelchair No. 1, wheelchair No. 2,
etc.). Utilization can be measured by incremental time periods
(e.g., minutes, hours, days, etc.) that each asset 105, 110, and
115 spends or is identified at a particular status indicator or
state. The utilization of the assets 105, 110, and 115 is
represented by the state or status indicators described above and
as shown in FIG. 3. An embodiment of calculating utilization
includes calculating or measuring the period of time that the asset
105, 110, and 115 spent in a recognized state. For example, the act
of calculating utilization can include calculating a percentage of
actual demand or utilization of the at least one asset 105, 110 and
115 over a time interval.
In one example, the utilization status is communicated with
location data for the asset 105, 110, and 115. In another example,
the acquired data for the utilization status can be equated to the
acquired location data of the asset 105, 110, and 115.
Predetermined status identifiers or index of utilization can be
stored in correlation or equated to various locations of the assets
105, 110, and 115. A status indicator can be "not in use" if a
location of one of the assets 105, 110, and 115 is in a storage
room, a dirty room, a cleaning room, or a service room while a
status indicator can be "in use" if the location of the asset 105,
110, and 115 is in a patient room. An embodiment of act 215
includes communicating the tracked use for illustration on the
display 280.
Examples of calculating utilization of the assets 105, 110, and 115
include calculating daily asset utilization that is generally equal
to a sum (in hours/day) of utilization or use of each type of asset
105, 110, and 115 divided by number of a type of assets 105, 110,
and 115; calculating a weekly asset utilization that is generally
equal to a sum of daily asset utilization for a calendar week for
each type of asset 105, 110, and 115 divided by sevens day/week;
calculating a monthly asset utilization that is generally equal to
sum of daily asset utilization of each type of asset 105, 110, and
115 for a calendar month divided by number of days in calendar
month; and calculating a yearly asset utilization that is generally
equal to sum of daily asset utilization for calendar year for each
type of asset 105, 110, and 115 divided by number of days in a
calendar year. Act 215 can further include normalizing the
utilization of the assets 105, 110, and 115 according to a number
of effective operational hours in a day at the entity (e.g., entity
is only open to the public for twelve hours per day).
Referring now to FIG. 3, one embodiment of the various states or
status indicators of utilization of each asset 105, 110, and 115 at
any given time as tracked in act 215 includes a USE status or state
220, a DIRTY state 225, a CLEANING state 230, an INVENTORY state
235, and a SERVICE state 240 of each of the assets 105, 110, and
115. The USE state 220 represents the assets 105, 110, and 115
being utilized by a patient or subject either in a patient room or
with the patient transitioning from one point or location to
another (e.g., for a walk, to get testing, etc.). The DIRTY state
225 represents the assets 105, 110, and 115 being temporarily
stored before being taken to a location of a CLEANING state 230 or,
if malfunctioning, to the SERVICE state 240. The CLEANING state 230
represents status of the assets 105, 110, and 115 in the process of
being cleaned of contamination or under routine maintenance so as
to be available for future utilization according to the USE state
220. The INVENTORY state 235 represents status of the assets 105,
110, and 115 that have previously been moved from the CLEANING
state 230 and are now in storage and ready for use in accordance
with the USE state 220 described above. The SERVICE state 240
represents status of the assets 105, 110, and 115 after
malfunctioning or requiring repair or to be discarded.
An embodiment of act 215 can include tracking the status indicator
of one or more of the assets 105, 110, and 115 having the unique
identifier on a continuous or periodic basis. In one example, the
status is communicated with location data for the asset 105, 110,
and 115 from the respective tracking elements 125, 130, and 135. In
another example, the status indicator can be automatically assigned
in accordance to a predetermined schedule correlating each of the
series of status indicators to one or more possible detected or
tracked locations of each of the assets 105, 110, and 115. For
example, a status indicator can be automatically assigned to the
unique identifier of the asset to be in the USE state if the
location of the asset 105, 110, and 115 is detected to be in a
patient room or location correlated according to a predetermined
schedule to the USE state. Alternatively, the status indicator can
be manually entered at the input 156 to the controller 150 for each
asset 105, 110, and 115.
Act 220 includes receiving data of predictive parameters associated
with a future business plan or direction correlated with a
predicted change in utilization of one or more of the assets 105,
110, and 115 looking forward in time. Examples of a predicted
business direction change in utilization of the assets includes
identifying a first factor representative of a predicted increase
or decrease in services associated with utilization of the assets,
identifying a second factor representative of a predicted change in
demand for utilization of the assets associated with a change in
competition or government regulations or in view of an expected
creation of a new department.
Act 230 includes receiving data of designated technical parameters
associated with each asset or type thereof. Examples of technical
parameters include expected expiration date, efficiency, purchase
cost to replace, rental or lease cost, dates and list of routine
maintenance items, expected release date of new model or evolving
technology, etc.
Act 235 includes integrating, cleaning, and pre-processing the
received data in a conventional manner to reduce the data as well
as to place or modify the data into a desired format. Reducing the
data can include vertical or horizontal reduction. An embodiment of
vertical reduction includes applying a reduction algorithm operable
to remove data for parameters from consideration that is calculated
to be not contributive to the calculation of utilization demand or
the calculation of the mode of procurement. An embodiment of
horizontal reduction includes aggregating data (e.g., averaging,
selecting minimum value, selecting maximum value, etc.).
Act 240 includes analyzing the data of act 235 to calculate and
display costs associated with current utilization of the assets
105, 110, and 115. Act 245 includes calculating a projected demand
or utilization of the at least one asset 105, 110, and 115. An
embodiment of the act 245 includes calculating a trend or slope of
the acquired or historical data for the measured utilization of the
asset 105, 110, and 115 over a selected time interval. The act 245
can include executing a linear or non-linear regression analysis, a
least squares analysis, or other conventional mathematical
techniques to calculate a slope (e.g., assets per day)
approximating the trend in the acquired data of the utilization of
the selected asset 105, 110, and 115 over the selected time
interval (e.g., 365 days, monthly). The act 245 can further include
aggregating (e.g., minimum, maximum, average, sum, count, etc.)
and/or normalizing the slope (e.g., to a value of one). The act 245
can further include multiplying the calculated slope with a
selected projected time interval so as to calculate the projected
demand or utilization of the asset 105, 110, 115 for the projected
time interval. The calculated projected demand can be adjusted with
one or more periodically upgraded factors for existing assets 105,
110 and 115 and one or more business direction factors. For
example, the upgrade factor can be adjusted based on comparison of
performance of existing to new assets 105, 110, and 115. The
factors can also be representative of a predicted useful life of
the asset 105, 110, 115. Values of the factors for the performance
or useful life can be updated based on the acquired data from the
assets 105, 110, and 115 over time.
An embodiment of calculating the projected demand looking forward
in time can also adjusted by a business adjustment factor
representative of a business direction as indicated by the user.
For example, the business adjustment factor can be calculated to
reflect inputted user information for expansion or shrinkage of the
facility, addition or removal of departments or services, local
competition, etc. received via the user input device 165. The
projected demand would then be calculated by multiplying the number
of assets 105, 110, and 115, the normalized value of the calculated
slope approximating the trend in demand, the upgrade factor, and
the business adjustment factor. Act 245 can further include
communicating the projected demand over the projected time interval
for illustration or display at the output device 170. An example of
the projected demand can be for a projected rental demand of the
selected asset 105, 110, and 115. An embodiment of act 245 can also
include dependence on parameters for demographic changes of each
department, growth of each department, etc., and adjusting the
projected demand in accordance or in correlation to the value of
the parameters. For example, act 245 can include calculating a
patient increase factor (PIF) for each department of the entity,
and multiplying the projected demand with the (PIF) to compute an
adjusted projected demand.
For example, the acquired asset utilization data per day is used in
an algorithm to calculate values of parameters in calculating a
prediction of a number of each type of asset 105, 110, and 115 in
the entity. According to this example, daily asset utilization data
acquired for a particular type of asset 105, 110, and 115 is
aggregated to three days, five days, thirty days, and twelve
months. The daily utilization, three-day utilization, five-day
utilization, thirty-day utilization, and twelve-month utilization
are implemented as parameters in the algorithm to predict a future
need of the assets 105, 110, and 115. Other additional parameters
implemented to predict future needs of assets 105, 110, and 115
include evolution parameters of the assets 105, 110, and 115,
financial parameters, commercial parameters, entity growth
parameters, etc. The above-described forecast or predicted demands
or needs of each asset 105, 110, and 115 or asset type thereof is
aggregated for illustration to the user.
Act 250 includes comparing the predicted or future need of each
type of asset with the number of current assets 105, 110, and 115.
An embodiment of act 250 includes calculating whether a
rearrangement or movement of surplus of assets 105, 110, and 115 at
certain location or department can meet the predicted demand at
another location or department.
If it is calculated or identified that there is an insufficient
number of one or types of assets 105, 110, and 115 to satisfy a
predicted need or demand at a location or department as calculated
in act 248, then act 255 includes calculating a mode (e.g.,
purchase, lease, rent, etc.) to procure or acquire assets 105, 110,
and 115. An embodiment of act 255 includes calculating a predicted
or projected cost for the projected demand of the asset 105, 110,
and 115 for various modes of procurement. An embodiment of the act
255 can include comparing the projected costs for several
alternatives manners of procurement to meet the projected demand.
For example, the act 255 can include receiving a rental rate and at
least one rental rule for the at least one asset, and multiplying a
projected rental cost based on the rental rate and the projected
rental demand for the selected time interval. The act 255 can also
include receiving a purchase cost and a depreciation rate of the at
least one asset 105, 110, and 115, and calculating a projected
value of the least one asset 105, 110, and 115 equal including the
purchase cost less the depreciation rate multiplied by the
projected rental time interval. An embodiment of act 255 can also
include communicating the at least one projected cost for each
manner of procurement for illustration or display at the output
device 170. An embodiment of act 255 can also include calculating a
recommended number of assets of a particular type to be procured
via a combination of purchasing, renting, or leasing in accordance
to the calculated trend in utilization described above.
Act 255 can also include comparing the analyzed data calculated
above for illustration to the user. An embodiment of the act 255
can include illustrating the projected purchase value of the least
one asset 105, 110, and 115 in comparison to the rental cost for
the projected rental time interval. Act 255 can also include
comparing one or more of the calculated utilization, projected
demand, and projected cost to other data acquired by other
facilities (e.g., different healthcare networks, different
hospitals, etc.) or clusters thereof of similar characteristics for
comparison, the other data stored at the embodiment of the
controller 150 that is in communication with a series of facilities
or entities of different ownership or corporation.
An embodiment of act 255 can further include creating and executing
a recurrent neural network classifier algorithm operable to
generate the output of the best mode of procurement of assets 105,
110, and 115 for illustration to the operator. The classifier
algorithm is configured to produce a binary output or result
representative of a best mode of whether to procure the assets 105,
110, and 115 via purchase versus rent/lease. Parameters or factors
incorporated in the classifier algorithm to calculate the best mode
(e.g., purchase, lease, rent, etc.) to procure the assets 105, 110,
and 115 includes price (purchase price versus rent/lease cost),
parameter representative of an availability of the asset, buying a
parameter representative of a favorability of terms of purchase in
comparison to terms of rent/lease, parameter representative of a
degree of change in product evolution versus current asset, a
parameter representative of a business direction of the entity
(e.g., expansion or contraction of budget), change in tax laws,
change in inflation, etc.
FIG. 4 illustrates a schematic diagram of one embodiment of the
recurrent neural classifier algorithm of act 255. The embodiment of
the classifier algorithm employs a Recurrent Neural Network
technique to calculate a best mode to procure the assets 105, 110,
and 115. Yet, alternative fuzzy neural network techniques or a Back
Propagation trained Multilayer Perception (MLP) technique can be
used or in combination with the described recurrent neural
classifier algorithm of act 255.
The embodiment of the recurrent neural network algorithm generally
includes a plurality of input layer nodes 265, hidden layer or
empirical nodes 270, and output layer of nodes 275 arranged in
representation of a mathematical model to calculate a best mode
(e.g., purchase, lease, rent, etc.) to procure the assets 105, 110,
and 115. Of course, the number or arrangement of the nodes in each
layer 265, 270, and 275 can vary.
Each node generally represents a mathematical formula of comparison
to produce a binary result or value. For example, one embodiment of
the input layer of nodes 265 includes a series of nodes 280, 282,
284, 286 each representative of a summation of input values
received or acquired for the following group of parameters: one-day
average node 280, three-day average node 282, five-day average node
284, thirty-day average node 286 representative of measured
parameters of asset utilization. Yet, the input layer of nodes can
include additional nodes representative of other parameters of
asset utilization (e.g., a three-month average (a90), and one-year
average (a356) of measured asset utilization). The result or output
communicated from each of the input nodes 280, 282, 284, and 286 is
generally representative of a state of the node. One embodiment of
the states of the nodes includes binary values 0, 1, and 2. Yet,
values of the states of the nodes can vary.
The embodiment of nodes 290, 292, 294, and 296 comprising the
hidden layer of nodes 270 are joined or coupled by a series of
connections 300 to receive the output or states of the nodes in the
input layer 265. Each connection 300 leading from the input nodes
280, 282, 284, 286 generally represents an assigned empirical value
of a weight to be multiplied by each value or state of the input
layer node 280, 282, 284, and 286 that the connections 300 leads
from. Each node 290, 292, 294, 296 in the hidden layer 270 is also
coupled by a connection 300 to itself. An embodiment of each node
290, 292, 294 and 296 of the hidden layer of nodes 270 and the
output layer of nodes 275 is representative of a summation of all
input values or states correlated to the input nodes 280, 282, 284
and 286 joined by connections 300 thereto multiplied by the
above-described empirical values of weights of the joining
connections 300 from the respective input nodes 280, 282, 284, 286
to the nodes 290, 292, 294, 296 of the hidden layer of nodes 270
for comparison relative to a predetermined threshold value.
As a specific example, node 290 represents the following
mathematical summation: (node 280*w1+node 282*w2+node 284 w3+node
286 w4+node 290*w5+node 292*w6+node 294*w7+node 296*w8+ . . . node
n*wn) for comparison to a predetermined threshold, where w1, w2,
w3, w4, . . . , wn are assigned empirical values represented by the
connections 300 joining the respective input nodes 280, 282, 284,
286, 290, 292, 294, 296, etc. to the hidden layer node 290. The
other nodes in the hidden layer and the output layer represent
similar mathematical functions or formulas. Alternatively, it
should be understood that the nodes can represent other types of
mathematical functions, formulas or equations than the subject
matter described herein.
In a similar manner to that described above, the result or outcome
of the comparison at the output node 320 of the output layer 274 is
equated to a binary value or state of 0, 1 or 2 of the node. Each
binary value or state of the output layer node 320 is correlated or
associated to a best mode (e.g., purchase=0 versus lease/rent=1, or
other procurement means=2) to procure or acquire the assets 105,
110, and 115.
Referring to FIG. 5, an embodiment of the act 255 further includes
an act 350 of executing a back propagation technique to calculate
the empirical values or weights represented by the connections 300.
Referred to as training the model of the algorithm, act 355
includes calculating empirical values (e.g., w1, w2, w3, w4, etc.)
for the connections 300 in a backward manner or fashion based on
acquired predetermined acceptable errors for historical data of
input values known to produce known outcomes (illustrated by
reference 360) of the best mode or manner to procure assets 105,
110, and 115. The act of training 255 creates or adjusts the
algorithm to be consistent with previous/past decisions of the best
mode to procure projected demands of assets 105, 110, 115.
Calculated values for the weights as determined using the back
propagation technique dependent on historical outcomes are then
adopted as the current model of the algorithm to calculate or
generate outcomes of the manner to procure a projected demand of
the assets 105, 110, and 115.
Act 365 includes validating the model. An embodiment of act 365
includes testing whether other acquired historical input values
will produce consistent historical outcomes of the mode or manner
to procure the assets 105, 110, and 115. A calculated number of
false positives and negatives of outcomes to procure the assets
105, 110, and 115 are compared to a threshold indicative of whether
the model of the algorithm is acceptable for deployment or not
acceptable. Act 370 includes an optional step or act of validating
the model in the field by employing the model to calculate outcomes
of whether to purchase and lease/rent for random input. Act 375
includes deploying the model of the algorithm to be used by
customers in calculating the mode or manner to procure a predicted
need for assets 105, 110, and 115.
Referring back to FIG. 2, Act 380 includes displaying the mode or
manner to procure the predicted need for the assets 105, 110, and
115 at the output device 170 to the user. Act 385 is the end of the
method 200.
A technical effect of the system 100 and method 200 described above
is to execute a calculation of a need of one or more assets based
on historical data of asset utilization, clustering or segmentation
of individual entities (third party entities such as hospitals,
clinics, etc. of similar infrastructure and willing to share data
and analytic output generated using the system and method), and
values indicative or representative of a business direction of the
entity looking toward the future. Another technical effect includes
generating an output for a current state of asset utilization
compared to third party entities with similar infrastructure and
willing to share asset utilization data and analytic output using
the system and method. Yet another technical effect includes
generating plans to improve utilization of existing assets, as well
as recommending disposal of existing assets and procurement of new
assets.
This written description uses examples to disclose the invention,
including the best mode, and also to enable any person skilled in
the art to make and use the invention. The patentable scope of the
invention is defined by the claims, and may include other examples
that occur to those skilled in the art. Such other examples are
intended to be within the scope of the claims if they have
structural elements that do not differ from the literal language of
the claims, or if they include equivalent structural elements with
insubstantial differences from the literal languages of the
claims.
* * * * *
References